Evaluating Impact Using Time-Series Data

• Published in: Trends in ecology & evolution (Amsterdam) 2021-03, Vol.36 (3), p.196-205
• Main Authors: Wauchope, Hannah S., Amano, Tatsuya, Geldmann, Jonas, Johnston, Alison, Simmons, Benno I., Sutherland, William J., Jones, Julia P.G.
• Format: Article
• Language: English
• Subjects: before-after-control-intervention; Biodiversity; causal inference; Conservation of Natural Resources; counterfactual; difference in differences; Ecology; interrupted time series; longitudinal data
• ISSN: 0169-5347; 1872-8383
• DOI: 10.1016/j.tree.2020.11.001

Humanity’s impact on the environment is increasing, as are strategies to conserve biodiversity, but a lack of understanding about how interventions affect ecological and conservation outcomes hampers decision-making. Time series are often used to assess impacts, but ecologists tend to compare average values from before to after an impact; overlooking the potential for the intervention to elicit a change in trend. Without methods that allow for a range of responses, erroneous conclusions can be drawn, especially for large, multi-time-series datasets, which are increasingly available. Drawing on literature in other disciplines and pioneering work in ecology, we present a standardised framework to robustly assesses how interventions, like natural disasters or conservation policies, affect ecological time series. Ecologists have called for more robust studies on the impact of conservation interventions, or environmental shocks, on outcomes of interest, such as populations, habitat loss, or pressures.Time-series data are increasingly available and can, if appropriately analysed, allow such causal inferences.However, there are important pitfalls that make large-scale analyses involving multiple time series problematic.There has been progress in a range of fields, but the literature is fragmented and not all is easily accessible to ecologists.A framework is presented, with clear and consistent terminology, to support ecologists to conduct effective impact evaluation with time-series data. This will allow them to contribute to better-informed environmental management decisions.